blackSettings.
To expose the effect of different priors for image inpainting, we finetune the whole Places2 pre-trained LaMa baseline with 50,000 steps in ZeroRA on the Places2 subset of 25,000 training and 500 validation images over 5 scenes.
For each prior shown in Tab. REF , we train a TSR with specific loss function mentioned in Sec. REF . For combined priors shown in Tab. REF , we train one TSR for priors of the same loss function, e.g., CATS and lines are restored with the same TSR optimized with BCE, and gradients are restored with another TSR optimized with regularized \(\mathcal {L}_{grad}\)  in Eq.(REF ).
Other implemented details follow Sec. REF .
PSNR, SSIM [1]}, FID [2]}, and LPIPS [3]} are utilized as metrics.
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